Search Results for author:
Found 49 papers, 5 papers with code
A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model
We describe an accelerated hardware neuron being capable of emulating the adap-tive exponential integrate-and-fire neuron model.
Stochastic inference with deterministic spiking neurons
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference.
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation.
The high-conductance state enables neural sampling in networks of LIF neurons
The core idea of our approach is to separately consider two different "modes" of spiking dynamics: burst spiking and transient quiescence, in which the neuron does not spike for longer periods.
Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System
This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits.
Stochastic inference with spiking neurons in the high-conductance state
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro.
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate.
Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware
How spiking networks are able to perform probabilistic inference is an intriguing question, not only for understanding information processing in the brain, but also for transferring these computational principles to neuromorphic silicon circuits.
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites
This paper presents an extension of the BrainScaleS accelerated analog neuromorphic hardware model.
Spiking neurons with short-term synaptic plasticity form superior generative networks
In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively.
Full Wafer Redistribution and Wafer Embedding as Key Technologies for a Multi-Scale Neuromorphic Hardware Cluster
Together with the Kirchhoff-Institute for Physics(KIP) the Fraunhofer IZM has developed a full wafer redistribution and embedding technology as base for a large-scale neuromorphic hardware system.
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
Accelerated physical emulation of Bayesian inference in spiking neural networks
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices.
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency.
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing.
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain.
Ranked #3 on
Audio Classification
on SHD
Fast and energy-efficient neuromorphic deep learning with first-spike times
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance.
Structural plasticity on an accelerated analog neuromorphic hardware system
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations.
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control.
Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System
This paper presents verification and implementation methods that have been developed for the design of the BrainScaleS-2 65nm ASICs.
Accelerated Analog Neuromorphic Computing
The presented architecture is based upon a continuous-time, analog, physical model implementation of neurons and synapses, resembling an analog neuromorphic accelerator attached to build-in digital compute cores.
The Operating System of the Neuromorphic BrainScaleS-1 System
We present operation and development methodologies implemented for the BrainScaleS-1 neuromorphic architecture and walk through the individual components of BrainScaleS OS constituting the software stack for BrainScaleS-1 platform operation.
Extending BrainScaleS OS for BrainScaleS-2
BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing.
Surrogate gradients for analog neuromorphic computing
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time.
Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors.
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks.
Spiking neuromorphic chip learns entangled quantum states
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years.
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset.
BrainScaleS Large Scale Spike Communication using Extoll
The BrainScaleS Neuromorphic Computing System is currently connected to a compute cluster via Gigabit-Ethernet network technology.
The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity
Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives.
Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL
The BrainScaleS-2 (BSS-2) Neuromorphic Computing System currently consists of multiple single-chip setups, which are connected to a compute cluster via Gigabit-Ethernet network technology.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research.
Spiking Neural Network Equalization for IM/DD Optical Communication
A spiking neural network (SNN) equalizer model suitable for electronic neuromorphic hardware is designed for an IM/DD link.
Spiking Neural Network Equalization on Neuromorphic Hardware for IM/DD Optical Communication
A spiking neural network (SNN) non-linear equalizer model is implemented on the mixed-signal neuromorphic hardware system BrainScaleS-2 and evaluated for an IM/DD link.
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain.
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware
The extent of memory about past inputs is commonly quantified by the autocorrelation time of collective dynamics.
An accurate and flexible analog emulation of AdEx neuron dynamics in silicon
Analog neuromorphic hardware promises fast brain emulation on the one hand and an efficient implementation of novel, brain-inspired computing paradigms on the other.
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2
Neuromorphic systems require user-friendly software to support the design and optimization of experiments.
Event-based Backpropagation for Analog Neuromorphic Hardware
Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances.
Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication
The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2).
From Clean Room to Machine Room: Commissioning of the First-Generation BrainScaleS Wafer-Scale Neuromorphic System
The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons.
Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware
The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico.
Emulating insect brains for neuromorphic navigation
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment.
jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware
Traditional neuromorphic hardware architectures rely on event-driven computation, where the asynchronous transmission of events, such as spikes, triggers local computations within synapses and neurons.
Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system.
